In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS)
system using Deep Learning (DL) algorithms and deploy it in an edge computing
environment. We establish a multi-layer edge computing architecture and a
distributed DL training model for the DIVS system. The DIVS system can migrate
computing workloads from the network center to network edges to reduce huge
network communication overhead and provide low-latency and accurate video
analysis solutions. We implement the proposed DIVS system and address the
problems of parallel training, model synchronization, and workload balancing.
Task-level parallel and model-level parallel training methods are proposed to
further accelerate the video analysis process. In addition, we propose a model
parameter updating method to achieve model synchronization of the global DL
model in a distributed EC environment. Moreover, a dynamic data migration
approach is proposed to address the imbalance of workload and computational
power of edge nodes. Experimental results showed that the EC architecture can
provide elastic and scalable computing power, and the proposed DIVS system can
efficiently handle video surveillance and analysis tasks.

本文提出了一种基于深度学习算法的分布式智能视频监控系统，并将其部署在边缘计算环境中，在边缘设备上进行任务级并行和模型级并行训练，从而实现了低延迟和精确的视频分析解决方案，同时实现了模型同步和工作负载平衡。实验结果表明，该分布式边缘计算架构可以提供弹性和可扩展的计算能力，并且提出的分布式智能视频监控系统可以高效地处理视频监控和分析任务。